Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "35" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 50 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 48 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459865 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.967997 | 3.516952 | 2.374233 | 8.918071 | 4.742289 | 4.751411 | 4.923008 | 1.409489 | 0.6320 | 0.6651 | 0.3837 | nan | nan |
| 2459864 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.402308 | 2.739545 | -0.741439 | 5.458762 | 2.494369 | 3.077165 | 11.484949 | 1.124569 | 0.6035 | 0.6320 | 0.4362 | nan | nan |
| 2459863 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 2.525136 | 0.673677 | -1.267490 | -1.451622 | -0.659335 | -0.647573 | 3.671752 | -0.702633 | 0.6003 | 0.6261 | 0.4216 | nan | nan |
| 2459862 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.571587 | 1.166182 | -0.340867 | 7.000387 | 4.091352 | 2.846085 | 1.312650 | -0.153737 | 0.5799 | 0.6596 | 0.4399 | nan | nan |
| 2459861 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.051869 | -0.088833 | -0.556281 | -2.065407 | -1.749952 | -1.665314 | 5.952491 | -0.237775 | 0.6200 | 0.6410 | 0.4361 | nan | nan |
| 2459860 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.473432 | -0.365746 | 1.298447 | 7.449805 | 3.132243 | 3.938525 | 3.940005 | -0.659618 | 0.6271 | 0.6402 | 0.4370 | nan | nan |
| 2459859 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.996454 | -0.291767 | -0.123074 | -2.278446 | 0.339822 | -1.685670 | 1.178590 | -0.054751 | 0.6263 | 0.6464 | 0.4341 | nan | nan |
| 2459858 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.852818 | -0.157712 | -0.411706 | -2.449081 | -1.694317 | -2.013424 | 5.574541 | -0.092626 | 0.6408 | 0.6505 | 0.4436 | 2.447880 | 2.601719 |
| 2459857 | not_connected | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 3.492417 | 0.849837 | 2.142813 | 2.380752 | 1.046394 | -0.540921 | 1.438503 | -2.210418 | 0.0290 | 0.0292 | 0.0005 | nan | nan |
| 2459856 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.607974 | 0.652797 | -0.958440 | 5.630443 | 4.263983 | 1.030568 | 1.791026 | -0.586575 | 0.6274 | 0.6691 | 0.4338 | 2.423846 | 2.653085 |
| 2459855 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.148261 | 0.502823 | 0.209023 | 6.139351 | 0.086040 | 0.583900 | 2.857717 | -0.711028 | 0.6043 | 0.6752 | 0.4711 | 2.719352 | 2.920611 |
| 2459854 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.226375 | 0.100135 | 0.983933 | 5.855953 | 0.455754 | 0.533359 | 5.025464 | 0.305822 | 0.6442 | 0.7144 | 0.4587 | 2.280927 | 2.360096 |
| 2459853 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.538578 | 0.286348 | 3.280390 | 9.364082 | 1.348007 | 2.099451 | 7.156600 | -0.081770 | 0.6667 | 0.6555 | 0.4526 | 2.817366 | 3.047641 |
| 2459852 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.025331 | 1.470292 | 3.190974 | 8.691133 | 1.828017 | 5.352549 | 2.880341 | 5.710893 | 0.7743 | 0.8086 | 0.2760 | 4.830338 | 5.483250 |
| 2459851 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.657644 | 3.130349 | 0.275280 | 8.138906 | 10.050139 | 11.891126 | 5.443827 | 8.403209 | 0.6687 | 0.7098 | 0.3752 | 2.264655 | 2.535631 |
| 2459850 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.478256 | 1.414996 | 1.179211 | 6.940693 | 1.753993 | 5.309765 | 8.766453 | 5.439674 | 0.6667 | 0.7239 | 0.3774 | 2.581878 | 2.837892 |
| 2459849 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.329576 | -0.030601 | 1.640870 | 15.299833 | 2.364222 | 1.328892 | 4.849425 | -0.039725 | 0.6635 | 0.7193 | 0.3865 | 2.999991 | 3.193454 |
| 2459848 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.616151 | 0.288918 | 2.028607 | 10.778105 | 7.786274 | 2.677473 | 2.767300 | -0.391282 | 0.6349 | 0.7214 | 0.4063 | 2.568509 | 2.890394 |
| 2459847 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.781507 | 0.354487 | 2.897315 | 9.898209 | 5.008080 | 1.934918 | 3.546192 | -0.768376 | 0.6417 | 0.6512 | 0.4570 | 4.516327 | 4.617385 |
| 2459846 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.900613 | 3.045333 | 0.166639 | 8.159847 | 2.408039 | 5.268061 | 2.790109 | -0.410503 | 0.8015 | 0.6537 | 0.5070 | 2.766869 | 1.417140 |
| 2459845 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.461504 | 0.582340 | 2.158273 | 14.908804 | 4.279218 | 1.648105 | 2.112420 | -1.265090 | 0.6621 | 0.7301 | 0.4097 | 0.000000 | 0.000000 |
| 2459844 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 7.835868 | 4.028087 | 55.525076 | 55.668650 | 0.968416 | 0.431693 | 3.388481 | -2.053945 | 0.0281 | 0.0284 | 0.0006 | nan | nan |
| 2459843 | not_connected | 100.00% | 1.20% | 0.66% | 0.00% | 100.00% | 0.00% | 3.680015 | 4.186813 | 0.417580 | 15.149002 | 20.001279 | 5.526512 | 1.083987 | -2.656929 | 0.6745 | 0.7335 | 0.4300 | 5.057419 | 3.518508 |
| 2459842 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.143693 | 1.280090 | -2.810810 | -0.144192 | -1.581917 | -1.751993 | -0.089784 | -2.326146 | 0.7361 | 0.6111 | 0.3004 | 3.700625 | 3.064528 |
| 2459841 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 40.725691 | 44.737522 | 123.135288 | 121.933350 | 169.895447 | 76.180809 | 11.517915 | 3.193536 | 0.7549 | 0.7343 | 0.3235 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 8.918071 | 5.967997 | 3.516952 | 2.374233 | 8.918071 | 4.742289 | 4.751411 | 4.923008 | 1.409489 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Discontinuties | 11.484949 | 2.739545 | 5.402308 | 5.458762 | -0.741439 | 3.077165 | 2.494369 | 1.124569 | 11.484949 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Discontinuties | 3.671752 | 2.525136 | 0.673677 | -1.267490 | -1.451622 | -0.659335 | -0.647573 | 3.671752 | -0.702633 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 7.000387 | 2.571587 | 1.166182 | -0.340867 | 7.000387 | 4.091352 | 2.846085 | 1.312650 | -0.153737 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Discontinuties | 5.952491 | -0.088833 | 1.051869 | -2.065407 | -0.556281 | -1.665314 | -1.749952 | -0.237775 | 5.952491 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 7.449805 | 1.473432 | -0.365746 | 1.298447 | 7.449805 | 3.132243 | 3.938525 | 3.940005 | -0.659618 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Discontinuties | 1.178590 | 0.996454 | -0.291767 | -0.123074 | -2.278446 | 0.339822 | -1.685670 | 1.178590 | -0.054751 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Discontinuties | 5.574541 | -0.157712 | 0.852818 | -2.449081 | -0.411706 | -2.013424 | -1.694317 | -0.092626 | 5.574541 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Shape | 3.492417 | 0.849837 | 3.492417 | 2.380752 | 2.142813 | -0.540921 | 1.046394 | -2.210418 | 1.438503 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 5.630443 | 2.607974 | 0.652797 | -0.958440 | 5.630443 | 4.263983 | 1.030568 | 1.791026 | -0.586575 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 6.139351 | 0.502823 | 2.148261 | 6.139351 | 0.209023 | 0.583900 | 0.086040 | -0.711028 | 2.857717 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 5.855953 | 0.100135 | 2.226375 | 5.855953 | 0.983933 | 0.533359 | 0.455754 | 0.305822 | 5.025464 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 9.364082 | 0.286348 | 1.538578 | 9.364082 | 3.280390 | 2.099451 | 1.348007 | -0.081770 | 7.156600 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 8.691133 | 3.025331 | 1.470292 | 3.190974 | 8.691133 | 1.828017 | 5.352549 | 2.880341 | 5.710893 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Temporal Variability | 11.891126 | 1.657644 | 3.130349 | 0.275280 | 8.138906 | 10.050139 | 11.891126 | 5.443827 | 8.403209 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Discontinuties | 8.766453 | 1.478256 | 1.414996 | 1.179211 | 6.940693 | 1.753993 | 5.309765 | 8.766453 | 5.439674 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 15.299833 | 2.329576 | -0.030601 | 1.640870 | 15.299833 | 2.364222 | 1.328892 | 4.849425 | -0.039725 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 10.778105 | 0.288918 | 2.616151 | 10.778105 | 2.028607 | 2.677473 | 7.786274 | -0.391282 | 2.767300 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 9.898209 | 0.354487 | 2.781507 | 9.898209 | 2.897315 | 1.934918 | 5.008080 | -0.768376 | 3.546192 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 8.159847 | 1.900613 | 3.045333 | 0.166639 | 8.159847 | 2.408039 | 5.268061 | 2.790109 | -0.410503 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 14.908804 | 0.582340 | 3.461504 | 14.908804 | 2.158273 | 1.648105 | 4.279218 | -1.265090 | 2.112420 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Power | 55.668650 | 7.835868 | 4.028087 | 55.525076 | 55.668650 | 0.968416 | 0.431693 | 3.388481 | -2.053945 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Variability | 20.001279 | 4.186813 | 3.680015 | 15.149002 | 0.417580 | 5.526512 | 20.001279 | -2.656929 | 1.083987 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | nn Shape | 1.280090 | -1.143693 | 1.280090 | -2.810810 | -0.144192 | -1.581917 | -1.751993 | -0.089784 | -2.326146 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | N06 | not_connected | ee Temporal Variability | 169.895447 | 40.725691 | 44.737522 | 123.135288 | 121.933350 | 169.895447 | 76.180809 | 11.517915 | 3.193536 |